Summary of Handling Ontology Gaps in Semantic Parsing, by Andrea Bacciu et al.
Handling Ontology Gaps in Semantic Parsing
by Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes the Hallucination Simulation Framework (HSF), a general setting for stimulating and analyzing Neural Semantic Parsing (NSP) model hallucinations. The majority of existing NSP models are developed under the closed-world assumption, which can lead to generating hallucinated outputs rather than admitting their lack of knowledge. This can result in wrong or potentially offensive responses to users. To address this issue, the authors assess state-of-the-art techniques for hallucination detection using KQA Pro as the benchmark dataset and present a novel hallucination detection strategy that exploits the computational graph of the NSP model. The proposed approach improves the F1-Score by ~21% in detecting ontology gaps, ~24% in recognizing out-of-domain utterances, and ~1% in identifying NSP errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s goal is to build trusted Question Answering agents by preventing NSP models from generating hallucinated outputs. The authors propose a framework called HSF that can be applied to any NSP task with a closed-ontology. They use KQA Pro as the benchmark dataset and assess state-of-the-art techniques for detecting hallucinations. The paper’s main contribution is a novel strategy for detecting hallucinations by analyzing the computational graph of the NSP model. |
Keywords
» Artificial intelligence » F1 score » Hallucination » Question answering » Semantic parsing
